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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 5.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,5 +1,6 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@a15381b23d3f6b59180e83a94a5279feccbf79a2#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/a15381b23d3f6b59180e83a94a5279feccbf79a2/gradio-4.44.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@bbf9ba7e997022960c621f72baa891185bd03732#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/bbf9ba7e997022960c621f72baa891185bd03732/gradio-5.0.0-py3-none-any.whl
 pillow
 torch
-torchvision
+torchvision
+requests

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_2"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pillow torch torchvision"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_2/files/imagenet_labels.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import requests\n", "import torch\n", "from PIL import Image\n", "from torchvision import transforms\n", "\n", "import gradio as gr\n", "\n", "model = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", "    inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n", "    inp = transforms.ToTensor()(inp).unsqueeze(0)\n", "    with torch.no_grad():\n", "        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "inputs = gr.Image()\n", "outputs = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_2"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pillow torch torchvision requests "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_2/files/imagenet_labels.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import requests\n", "import torch\n", "from PIL import Image\n", "from torchvision import transforms\n", "\n", "import gradio as gr\n", "\n", "model = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", "    inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n", "    inp = transforms.ToTensor()(inp).unsqueeze(0)\n", "    with torch.no_grad():\n", "        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", "    return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "inputs = gr.Image()\n", "outputs = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}